Overview

Dataset statistics

Number of variables19
Number of observations390
Missing cells37
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory58.0 KiB
Average record size in memory152.3 B

Variable types

Numeric15
Categorical4

Alerts

BMI is highly overall correlated with Weight and 2 other fieldsHigh correlation
CLASS is highly overall correlated with glyhb and 1 other fieldsHigh correlation
Gender is highly overall correlated with HeightHigh correlation
Height is highly overall correlated with GenderHigh correlation
Weight is highly overall correlated with BMI and 2 other fieldsHigh correlation
bp.1d is highly overall correlated with bp.1sHigh correlation
bp.1s is highly overall correlated with bp.1dHigh correlation
glyhb is highly overall correlated with CLASS and 1 other fieldsHigh correlation
hdl is highly overall correlated with ratioHigh correlation
hip is highly overall correlated with BMI and 2 other fieldsHigh correlation
ratio is highly overall correlated with hdlHigh correlation
stab.glu is highly overall correlated with CLASS and 1 other fieldsHigh correlation
waist is highly overall correlated with BMI and 2 other fieldsHigh correlation
frame has 11 (2.8%) missing valuesMissing
bp.1s has 5 (1.3%) missing valuesMissing
bp.1d has 5 (1.3%) missing valuesMissing
id has unique valuesUnique

Reproduction

Analysis started2024-02-21 01:55:45.937520
Analysis finished2024-02-21 01:56:54.449478
Duration1 minute and 8.51 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

id
Real number (ℝ)

UNIQUE 

Distinct390
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16003.762
Minimum1000
Maximum41752
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-02-21T01:56:54.639627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1250.9
Q14792.25
median15769.5
Q320334.25
95-th percentile41026.3
Maximum41752
Range40752
Interquartile range (IQR)15542

Descriptive statistics

Standard deviation11828.808
Coefficient of variation (CV)0.73912674
Kurtosis0.113475
Mean16003.762
Median Absolute Deviation (MAD)5486.5
Skewness0.81657101
Sum6241467
Variance1.399207 × 108
MonotonicityStrictly increasing
2024-02-21T01:56:54.958691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 1
 
0.3%
17794 1
 
0.3%
20261 1
 
0.3%
20260 1
 
0.3%
20254 1
 
0.3%
17849 1
 
0.3%
17846 1
 
0.3%
17841 1
 
0.3%
17835 1
 
0.3%
17834 1
 
0.3%
Other values (380) 380
97.4%
ValueCountFrequency (%)
1000 1
0.3%
1001 1
0.3%
1002 1
0.3%
1003 1
0.3%
1005 1
0.3%
1008 1
0.3%
1011 1
0.3%
1015 1
0.3%
1016 1
0.3%
1022 1
0.3%
ValueCountFrequency (%)
41752 1
0.3%
41510 1
0.3%
41507 1
0.3%
41506 1
0.3%
41503 1
0.3%
41500 1
0.3%
41254 1
0.3%
41253 1
0.3%
41078 1
0.3%
41075 1
0.3%

chol
Real number (ℝ)

Distinct153
Distinct (%)39.3%
Missing1
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean207.27506
Minimum78
Maximum443
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-02-21T01:56:55.282535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum78
5-th percentile144.4
Q1179
median203
Q3229
95-th percentile290.8
Maximum443
Range365
Interquartile range (IQR)50

Descriptive statistics

Standard deviation44.71495
Coefficient of variation (CV)0.21572759
Kurtosis2.6657693
Mean207.27506
Median Absolute Deviation (MAD)25
Skewness0.9582663
Sum80630
Variance1999.4267
MonotonicityNot monotonic
2024-02-21T01:56:55.574907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
179 11
 
2.8%
204 9
 
2.3%
194 7
 
1.8%
219 7
 
1.8%
215 7
 
1.8%
199 6
 
1.5%
203 6
 
1.5%
181 5
 
1.3%
209 5
 
1.3%
173 5
 
1.3%
Other values (143) 321
82.3%
ValueCountFrequency (%)
78 1
0.3%
115 1
0.3%
118 1
0.3%
122 1
0.3%
128 1
0.3%
129 1
0.3%
132 2
0.5%
134 2
0.5%
135 2
0.5%
136 1
0.3%
ValueCountFrequency (%)
443 1
0.3%
404 1
0.3%
347 1
0.3%
342 1
0.3%
337 1
0.3%
322 1
0.3%
318 1
0.3%
307 1
0.3%
306 1
0.3%
305 1
0.3%

stab.glu
Real number (ℝ)

HIGH CORRELATION 

Distinct116
Distinct (%)29.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.33846
Minimum48
Maximum385
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-02-21T01:56:55.882023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum48
5-th percentile68
Q181
median90
Q3107.75
95-th percentile234.1
Maximum385
Range337
Interquartile range (IQR)26.75

Descriptive statistics

Standard deviation53.798188
Coefficient of variation (CV)0.50120141
Kurtosis7.9059129
Mean107.33846
Median Absolute Deviation (MAD)12
Skewness2.7111213
Sum41862
Variance2894.245
MonotonicityNot monotonic
2024-02-21T01:56:56.200149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85 18
 
4.6%
81 15
 
3.8%
92 14
 
3.6%
84 12
 
3.1%
87 12
 
3.1%
77 11
 
2.8%
83 11
 
2.8%
82 10
 
2.6%
76 10
 
2.6%
74 10
 
2.6%
Other values (106) 267
68.5%
ValueCountFrequency (%)
48 1
0.3%
52 1
0.3%
54 1
0.3%
56 2
0.5%
57 1
0.3%
58 1
0.3%
59 1
0.3%
60 1
0.3%
62 1
0.3%
64 2
0.5%
ValueCountFrequency (%)
385 1
0.3%
371 1
0.3%
369 1
0.3%
342 1
0.3%
341 1
0.3%
330 1
0.3%
299 1
0.3%
297 1
0.3%
279 1
0.3%
270 2
0.5%

hdl
Real number (ℝ)

HIGH CORRELATION 

Distinct75
Distinct (%)19.3%
Missing1
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean50.267352
Minimum12
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-02-21T01:56:56.512172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile29.4
Q138
median46
Q359
95-th percentile85.6
Maximum120
Range108
Interquartile range (IQR)21

Descriptive statistics

Standard deviation17.301317
Coefficient of variation (CV)0.34418595
Kurtosis2.109795
Mean50.267352
Median Absolute Deviation (MAD)10
Skewness1.2272745
Sum19554
Variance299.33556
MonotonicityNot monotonic
2024-02-21T01:56:56.839308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46 21
 
5.4%
44 20
 
5.1%
36 20
 
5.1%
34 18
 
4.6%
42 15
 
3.8%
40 14
 
3.6%
37 11
 
2.8%
54 11
 
2.8%
58 9
 
2.3%
48 9
 
2.3%
Other values (65) 241
61.8%
ValueCountFrequency (%)
12 1
 
0.3%
14 1
 
0.3%
23 1
 
0.3%
24 5
1.3%
25 1
 
0.3%
26 3
0.8%
28 4
1.0%
29 4
1.0%
30 5
1.3%
31 5
1.3%
ValueCountFrequency (%)
120 1
 
0.3%
118 1
 
0.3%
117 1
 
0.3%
114 1
 
0.3%
110 1
 
0.3%
108 1
 
0.3%
100 1
 
0.3%
94 1
 
0.3%
92 4
1.0%
91 1
 
0.3%

ratio
Real number (ℝ)

HIGH CORRELATION 

Distinct69
Distinct (%)17.7%
Missing1
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean4.5264781
Minimum1.5
Maximum19.299999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-02-21T01:56:57.186445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile2.5
Q13.2
median4.1999998
Q35.4000001
95-th percentile7.3000002
Maximum19.299999
Range17.799999
Interquartile range (IQR)2.2

Descriptive statistics

Standard deviation1.7384799
Coefficient of variation (CV)0.384069
Kurtosis13.522397
Mean4.5264781
Median Absolute Deviation (MAD)1.0999999
Skewness2.2409389
Sum1760.8
Variance3.0223125
MonotonicityNot monotonic
2024-02-21T01:56:57.477528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.599999905 15
 
3.8%
5.300000191 14
 
3.6%
3 14
 
3.6%
3.099999905 13
 
3.3%
2.900000095 12
 
3.1%
4.300000191 12
 
3.1%
4.099999905 12
 
3.1%
3.299999952 11
 
2.8%
3.900000095 11
 
2.8%
5.099999905 11
 
2.8%
Other values (59) 264
67.7%
ValueCountFrequency (%)
1.5 1
 
0.3%
1.899999976 1
 
0.3%
2 1
 
0.3%
2.099999905 1
 
0.3%
2.200000048 4
 
1.0%
2.299999952 2
 
0.5%
2.400000095 6
1.5%
2.5 6
1.5%
2.599999905 10
2.6%
2.700000048 7
1.8%
ValueCountFrequency (%)
19.29999924 1
0.3%
12.19999981 1
0.3%
10.60000038 1
0.3%
9.399999619 1
0.3%
8.899999619 2
0.5%
8.699999809 1
0.3%
8.300000191 1
0.3%
8 1
0.3%
7.900000095 2
0.5%
7.800000191 2
0.5%

glyhb
Real number (ℝ)

HIGH CORRELATION 

Distinct239
Distinct (%)61.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5897692
Minimum2.6800001
Maximum16.110001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-02-21T01:56:57.763163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2.6800001
5-th percentile3.75
Q14.3800001
median4.8400002
Q35.5999999
95-th percentile10.9165
Maximum16.110001
Range13.430001
Interquartile range (IQR)1.2199998

Descriptive statistics

Standard deviation2.2425948
Coefficient of variation (CV)0.40119632
Kurtosis5.1064866
Mean5.5897692
Median Absolute Deviation (MAD)0.55999994
Skewness2.2461247
Sum2180.01
Variance5.0292316
MonotonicityNot monotonic
2024-02-21T01:56:58.088518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.670000076 6
 
1.5%
4.400000095 6
 
1.5%
4.309999943 5
 
1.3%
4.409999847 5
 
1.3%
5.349999905 5
 
1.3%
4.659999847 5
 
1.3%
5.230000019 4
 
1.0%
4.610000134 4
 
1.0%
4.949999809 4
 
1.0%
4.380000114 4
 
1.0%
Other values (229) 342
87.7%
ValueCountFrequency (%)
2.680000067 1
0.3%
2.730000019 1
0.3%
2.849999905 2
0.5%
3.029999971 1
0.3%
3.329999924 1
0.3%
3.410000086 1
0.3%
3.440000057 1
0.3%
3.549999952 2
0.5%
3.559999943 1
0.3%
3.579999924 1
0.3%
ValueCountFrequency (%)
16.11000061 1
0.3%
15.52000046 1
0.3%
14.93999958 1
0.3%
14.31000042 1
0.3%
13.69999981 1
0.3%
13.63000011 1
0.3%
13.60000038 1
0.3%
13.06000042 1
0.3%
12.97000027 1
0.3%
12.73999977 1
0.3%

Location
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
Louisa
200 
Buckingham
190 

Length

Max length10
Median length6
Mean length7.9487179
Min length6

Characters and Unicode

Total characters3100
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBuckingham
2nd rowBuckingham
3rd rowBuckingham
4th rowBuckingham
5th rowBuckingham

Common Values

ValueCountFrequency (%)
Louisa 200
51.3%
Buckingham 190
48.7%

Length

2024-02-21T01:56:58.400748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-21T01:56:58.788102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
louisa 200
51.3%
buckingham 190
48.7%

Most occurring characters

ValueCountFrequency (%)
u 390
12.6%
i 390
12.6%
a 390
12.6%
L 200
 
6.5%
o 200
 
6.5%
s 200
 
6.5%
B 190
 
6.1%
c 190
 
6.1%
k 190
 
6.1%
n 190
 
6.1%
Other values (3) 570
18.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2710
87.4%
Uppercase Letter 390
 
12.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 390
14.4%
i 390
14.4%
a 390
14.4%
o 200
7.4%
s 200
7.4%
c 190
7.0%
k 190
7.0%
n 190
7.0%
g 190
7.0%
h 190
7.0%
Uppercase Letter
ValueCountFrequency (%)
L 200
51.3%
B 190
48.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 3100
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
u 390
12.6%
i 390
12.6%
a 390
12.6%
L 200
 
6.5%
o 200
 
6.5%
s 200
 
6.5%
B 190
 
6.1%
c 190
 
6.1%
k 190
 
6.1%
n 190
 
6.1%
Other values (3) 570
18.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
u 390
12.6%
i 390
12.6%
a 390
12.6%
L 200
 
6.5%
o 200
 
6.5%
s 200
 
6.5%
B 190
 
6.1%
c 190
 
6.1%
k 190
 
6.1%
n 190
 
6.1%
Other values (3) 570
18.4%

Age
Real number (ℝ)

Distinct68
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.774359
Minimum19
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-02-21T01:56:59.210804image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile22
Q134
median44.5
Q360
95-th percentile76
Maximum92
Range73
Interquartile range (IQR)26

Descriptive statistics

Standard deviation16.435911
Coefficient of variation (CV)0.35138721
Kurtosis-0.6631611
Mean46.774359
Median Absolute Deviation (MAD)13.5
Skewness0.33290667
Sum18242
Variance270.13919
MonotonicityNot monotonic
2024-02-21T01:56:59.723617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40 16
 
4.1%
36 13
 
3.3%
41 12
 
3.1%
43 12
 
3.1%
37 11
 
2.8%
38 11
 
2.8%
63 11
 
2.8%
60 10
 
2.6%
50 10
 
2.6%
20 10
 
2.6%
Other values (58) 274
70.3%
ValueCountFrequency (%)
19 2
 
0.5%
20 10
2.6%
21 6
1.5%
22 5
1.3%
23 7
1.8%
24 1
 
0.3%
25 4
 
1.0%
26 6
1.5%
27 9
2.3%
28 8
2.1%
ValueCountFrequency (%)
92 1
 
0.3%
91 1
 
0.3%
89 1
 
0.3%
84 1
 
0.3%
83 1
 
0.3%
82 2
0.5%
81 1
 
0.3%
80 2
0.5%
79 2
0.5%
78 4
1.0%

Gender
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
female
228 
male
162 

Length

Max length6
Median length6
Mean length5.1692308
Min length4

Characters and Unicode

Total characters2016
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfemale
2nd rowfemale
3rd rowfemale
4th rowmale
5th rowmale

Common Values

ValueCountFrequency (%)
female 228
58.5%
male 162
41.5%

Length

2024-02-21T01:57:00.240191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-21T01:57:00.658334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
female 228
58.5%
male 162
41.5%

Most occurring characters

ValueCountFrequency (%)
e 618
30.7%
m 390
19.3%
a 390
19.3%
l 390
19.3%
f 228
 
11.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2016
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 618
30.7%
m 390
19.3%
a 390
19.3%
l 390
19.3%
f 228
 
11.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 2016
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 618
30.7%
m 390
19.3%
a 390
19.3%
l 390
19.3%
f 228
 
11.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2016
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 618
30.7%
m 390
19.3%
a 390
19.3%
l 390
19.3%
f 228
 
11.3%

Height
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)5.7%
Missing3
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean65.979328
Minimum52
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-02-21T01:57:01.014393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum52
5-th percentile60
Q163
median66
Q369
95-th percentile72
Maximum76
Range24
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.9267366
Coefficient of variation (CV)0.05951465
Kurtosis-0.21097518
Mean65.979328
Median Absolute Deviation (MAD)3
Skewness0.016563487
Sum25534
Variance15.419261
MonotonicityNot monotonic
2024-02-21T01:57:01.301313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
63 40
10.3%
69 37
9.5%
67 36
9.2%
65 33
8.5%
62 32
8.2%
64 32
8.2%
66 31
7.9%
68 26
 
6.7%
70 22
 
5.6%
71 21
 
5.4%
Other values (12) 77
19.7%
ValueCountFrequency (%)
52 1
 
0.3%
55 1
 
0.3%
56 1
 
0.3%
58 3
 
0.8%
59 9
 
2.3%
60 10
 
2.6%
61 20
5.1%
62 32
8.2%
63 40
10.3%
64 32
8.2%
ValueCountFrequency (%)
76 2
 
0.5%
75 3
 
0.8%
74 5
 
1.3%
73 8
 
2.1%
72 14
 
3.6%
71 21
5.4%
70 22
5.6%
69 37
9.5%
68 26
6.7%
67 36
9.2%

Weight
Real number (ℝ)

HIGH CORRELATION 

Distinct203
Distinct (%)52.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean176.20256
Minimum103.5
Maximum322
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-02-21T01:57:01.593806image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum103.5
5-th percentile119.45
Q1149.5
median172.25
Q3196.75
95-th percentile250.55
Maximum322
Range218.5
Interquartile range (IQR)47.25

Descriptive statistics

Standard deviation39.300863
Coefficient of variation (CV)0.22304365
Kurtosis0.84381342
Mean176.20256
Median Absolute Deviation (MAD)23.75
Skewness0.77717299
Sum68719
Variance1544.5578
MonotonicityNot monotonic
2024-02-21T01:57:01.879543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
173.5 8
 
2.1%
165 6
 
1.5%
120 5
 
1.3%
168.5 5
 
1.3%
163 5
 
1.3%
199 5
 
1.3%
180 5
 
1.3%
159 5
 
1.3%
185 5
 
1.3%
183 5
 
1.3%
Other values (193) 336
86.2%
ValueCountFrequency (%)
103.5 1
 
0.3%
104.5 1
 
0.3%
105 1
 
0.3%
107.5 1
 
0.3%
109 2
0.5%
110 1
 
0.3%
111 1
 
0.3%
113 1
 
0.3%
114 1
 
0.3%
115 4
1.0%
ValueCountFrequency (%)
322 1
0.3%
317 1
0.3%
308 1
0.3%
285.5 1
0.3%
283.5 1
0.3%
280.5 1
0.3%
279.5 1
0.3%
277 1
0.3%
275 1
0.3%
272.5 1
0.3%

BMI
Real number (ℝ)

HIGH CORRELATION 

Distinct356
Distinct (%)92.0%
Missing3
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean28.597051
Minimum16.00193
Maximum55.26514
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-02-21T01:57:02.175318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum16.00193
5-th percentile19.539222
Q124.132565
median27.64079
Q331.93694
95-th percentile40.612259
Maximum55.26514
Range39.26321
Interquartile range (IQR)7.804375

Descriptive statistics

Standard deviation6.4398836
Coefficient of variation (CV)0.22519397
Kurtosis0.93633767
Mean28.597051
Median Absolute Deviation (MAD)3.84694
Skewness0.8416397
Sum11067.059
Variance41.472101
MonotonicityNot monotonic
2024-02-21T01:57:02.486120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.61444 3
 
0.8%
30.69459 3
 
0.8%
29.38395 3
 
0.8%
27.17097 3
 
0.8%
21.43185 2
 
0.5%
29.0783 2
 
0.5%
28.18891 2
 
0.5%
27.24125 2
 
0.5%
34.95163 2
 
0.5%
25.05811 2
 
0.5%
Other values (346) 363
93.1%
(Missing) 3
 
0.8%
ValueCountFrequency (%)
16.00193 1
0.3%
16.34353 1
0.3%
17.21633 1
0.3%
17.3598 1
0.3%
17.91391 1
0.3%
17.93431 1
0.3%
18.02124 1
0.3%
18.13657 1
0.3%
18.55946 1
0.3%
18.7926 1
0.3%
ValueCountFrequency (%)
55.26514 1
0.3%
50.56853 1
0.3%
49.50579 1
0.3%
48.61723 1
0.3%
47.13746 1
0.3%
45.47868 1
0.3%
45.07773 1
0.3%
44.30321 1
0.3%
44.2077 1
0.3%
43.39506 1
0.3%

frame
Categorical

MISSING 

Distinct3
Distinct (%)0.8%
Missing11
Missing (%)2.8%
Memory size3.2 KiB
medium
178 
small
102 
large
99 

Length

Max length6
Median length5
Mean length5.469657
Min length5

Characters and Unicode

Total characters2073
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmedium
2nd rowlarge
3rd rowlarge
4th rowlarge
5th rowmedium

Common Values

ValueCountFrequency (%)
medium 178
45.6%
small 102
26.2%
large 99
25.4%
(Missing) 11
 
2.8%

Length

2024-02-21T01:57:02.779392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-21T01:57:03.036089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
medium 178
47.0%
small 102
26.9%
large 99
26.1%

Most occurring characters

ValueCountFrequency (%)
m 458
22.1%
l 303
14.6%
e 277
13.4%
a 201
9.7%
d 178
 
8.6%
i 178
 
8.6%
u 178
 
8.6%
s 102
 
4.9%
r 99
 
4.8%
g 99
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2073
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
m 458
22.1%
l 303
14.6%
e 277
13.4%
a 201
9.7%
d 178
 
8.6%
i 178
 
8.6%
u 178
 
8.6%
s 102
 
4.9%
r 99
 
4.8%
g 99
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 2073
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
m 458
22.1%
l 303
14.6%
e 277
13.4%
a 201
9.7%
d 178
 
8.6%
i 178
 
8.6%
u 178
 
8.6%
s 102
 
4.9%
r 99
 
4.8%
g 99
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2073
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
m 458
22.1%
l 303
14.6%
e 277
13.4%
a 201
9.7%
d 178
 
8.6%
i 178
 
8.6%
u 178
 
8.6%
s 102
 
4.9%
r 99
 
4.8%
g 99
 
4.8%

bp.1s
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct71
Distinct (%)18.4%
Missing5
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean137.14805
Minimum90
Maximum250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-02-21T01:57:03.289590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum90
5-th percentile106.4
Q1121
median136
Q3148
95-th percentile179.8
Maximum250
Range160
Interquartile range (IQR)27

Descriptive statistics

Standard deviation22.997427
Coefficient of variation (CV)0.16768322
Kurtosis2.3299738
Mean137.14805
Median Absolute Deviation (MAD)14
Skewness1.0913893
Sum52802
Variance528.88167
MonotonicityNot monotonic
2024-02-21T01:57:03.586773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
140 35
 
9.0%
130 30
 
7.7%
110 26
 
6.7%
138 19
 
4.9%
120 18
 
4.6%
150 17
 
4.4%
142 16
 
4.1%
122 13
 
3.3%
136 13
 
3.3%
118 12
 
3.1%
Other values (61) 186
47.7%
ValueCountFrequency (%)
90 1
 
0.3%
98 1
 
0.3%
100 7
 
1.8%
102 2
 
0.5%
103 1
 
0.3%
104 3
 
0.8%
105 2
 
0.5%
106 3
 
0.8%
108 7
 
1.8%
110 26
6.7%
ValueCountFrequency (%)
250 1
 
0.3%
230 1
 
0.3%
220 1
 
0.3%
218 1
 
0.3%
212 1
 
0.3%
200 1
 
0.3%
199 1
 
0.3%
190 5
1.3%
186 1
 
0.3%
184 1
 
0.3%

bp.1d
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct56
Distinct (%)14.5%
Missing5
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean83.285714
Minimum48
Maximum124
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-02-21T01:57:03.896324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum48
5-th percentile62
Q175
median82
Q390
95-th percentile109.6
Maximum124
Range76
Interquartile range (IQR)15

Descriptive statistics

Standard deviation13.582366
Coefficient of variation (CV)0.16308158
Kurtosis0.077513885
Mean83.285714
Median Absolute Deviation (MAD)8
Skewness0.24526787
Sum32065
Variance184.48065
MonotonicityNot monotonic
2024-02-21T01:57:04.188684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 37
 
9.5%
80 28
 
7.2%
78 22
 
5.6%
82 21
 
5.4%
70 17
 
4.4%
86 16
 
4.1%
100 16
 
4.1%
88 15
 
3.8%
75 14
 
3.6%
72 14
 
3.6%
Other values (46) 185
47.4%
ValueCountFrequency (%)
48 1
 
0.3%
50 2
 
0.5%
52 1
 
0.3%
53 1
 
0.3%
56 1
 
0.3%
58 3
0.8%
59 1
 
0.3%
60 6
1.5%
61 1
 
0.3%
62 5
1.3%
ValueCountFrequency (%)
124 1
 
0.3%
122 1
 
0.3%
120 2
 
0.5%
118 2
 
0.5%
115 2
 
0.5%
114 1
 
0.3%
112 2
 
0.5%
110 9
2.3%
108 1
 
0.3%
106 1
 
0.3%

waist
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)7.7%
Missing2
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean37.896907
Minimum26
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-02-21T01:57:04.463062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile29
Q133
median37
Q341
95-th percentile48
Maximum56
Range30
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.7627235
Coefficient of variation (CV)0.15206316
Kurtosis-0.15563905
Mean37.896907
Median Absolute Deviation (MAD)4
Skewness0.46717168
Sum14704
Variance33.208983
MonotonicityNot monotonic
2024-02-21T01:57:05.346640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
37 31
 
7.9%
40 27
 
6.9%
38 27
 
6.9%
33 26
 
6.7%
36 25
 
6.4%
34 23
 
5.9%
39 23
 
5.9%
32 21
 
5.4%
31 21
 
5.4%
35 19
 
4.9%
Other values (20) 145
37.2%
ValueCountFrequency (%)
26 2
 
0.5%
27 1
 
0.3%
28 7
 
1.8%
29 11
2.8%
30 10
 
2.6%
31 21
5.4%
32 21
5.4%
33 26
6.7%
34 23
5.9%
35 19
4.9%
ValueCountFrequency (%)
56 1
 
0.3%
55 1
 
0.3%
53 2
 
0.5%
52 2
 
0.5%
51 4
1.0%
50 3
 
0.8%
49 6
1.5%
48 8
2.1%
47 7
1.8%
46 9
2.3%

hip
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)8.2%
Missing2
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean43.033505
Minimum30
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-02-21T01:57:05.626813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile35
Q139
median42
Q346
95-th percentile54
Maximum64
Range34
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.6492126
Coefficient of variation (CV)0.13127475
Kurtosis0.87799803
Mean43.033505
Median Absolute Deviation (MAD)3
Skewness0.79830729
Sum16697
Variance31.913603
MonotonicityNot monotonic
2024-02-21T01:57:05.884610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
39 35
 
9.0%
41 35
 
9.0%
40 33
 
8.5%
38 28
 
7.2%
43 28
 
7.2%
42 27
 
6.9%
47 22
 
5.6%
44 21
 
5.4%
46 20
 
5.1%
45 20
 
5.1%
Other values (22) 119
30.5%
ValueCountFrequency (%)
30 1
 
0.3%
32 1
 
0.3%
33 8
 
2.1%
34 5
 
1.3%
35 11
 
2.8%
36 6
 
1.5%
37 15
3.8%
38 28
7.2%
39 35
9.0%
40 33
8.5%
ValueCountFrequency (%)
64 1
 
0.3%
62 2
 
0.5%
60 1
 
0.3%
59 1
 
0.3%
58 5
1.3%
57 2
 
0.5%
56 1
 
0.3%
55 2
 
0.5%
54 6
1.5%
53 5
1.3%

time.ppn
Real number (ℝ)

Distinct60
Distinct (%)15.5%
Missing3
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean336.12403
Minimum5
Maximum1560
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-02-21T01:57:06.191656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile16.5
Q190
median240
Q3480
95-th percentile900
Maximum1560
Range1555
Interquartile range (IQR)390

Descriptive statistics

Standard deviation308.90428
Coefficient of variation (CV)0.91901873
Kurtosis1.2540882
Mean336.12403
Median Absolute Deviation (MAD)180
Skewness1.2730393
Sum130080
Variance95421.855
MonotonicityNot monotonic
2024-02-21T01:57:06.487983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
720 36
 
9.2%
60 35
 
9.0%
240 30
 
7.7%
180 25
 
6.4%
120 20
 
5.1%
300 19
 
4.9%
30 17
 
4.4%
90 16
 
4.1%
210 15
 
3.8%
150 12
 
3.1%
Other values (50) 162
41.5%
ValueCountFrequency (%)
5 1
 
0.3%
10 11
 
2.8%
15 8
 
2.1%
20 5
 
1.3%
30 17
4.4%
40 1
 
0.3%
45 3
 
0.8%
60 35
9.0%
75 1
 
0.3%
80 1
 
0.3%
ValueCountFrequency (%)
1560 1
 
0.3%
1440 3
0.8%
1320 1
 
0.3%
1260 1
 
0.3%
1200 1
 
0.3%
1170 1
 
0.3%
1140 1
 
0.3%
1080 1
 
0.3%
1020 4
1.0%
990 1
 
0.3%

CLASS
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
N
298 
Y
67 
P
 
25

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters390
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowY

Common Values

ValueCountFrequency (%)
N 298
76.4%
Y 67
 
17.2%
P 25
 
6.4%

Length

2024-02-21T01:57:06.776958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-21T01:57:07.042474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
n 298
76.4%
y 67
 
17.2%
p 25
 
6.4%

Most occurring characters

ValueCountFrequency (%)
N 298
76.4%
Y 67
 
17.2%
P 25
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 390
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 298
76.4%
Y 67
 
17.2%
P 25
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 390
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 298
76.4%
Y 67
 
17.2%
P 25
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 390
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 298
76.4%
Y 67
 
17.2%
P 25
 
6.4%

Interactions

2024-02-21T01:56:49.178917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:55:47.081076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:55:51.055926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:55:55.281659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:55:59.795874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:03.566227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:07.654221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:14.330060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:18.569655image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:23.450793image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:27.137109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:31.390486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:36.250443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:40.207540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:44.144678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:49.442225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:55:47.364657image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:55:51.308119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:55:55.616055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:00.061781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:03.836651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:08.030298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:14.732725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:18.806075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:23.705981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:27.390720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:31.655385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:36.506163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:40.500417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:44.391769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:49.696074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:55:47.615273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:55:51.583545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:55:55.946221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:00.310174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:04.103814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:08.402207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:15.076660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:19.061659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-02-21T01:56:09.283610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:15.792003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:19.530057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:24.455237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:28.156144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:32.474473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:37.243403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-02-21T01:56:45.154167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:50.469331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-02-21T01:55:52.327290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:55:57.098034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:01.076268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:04.885706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-02-21T01:56:16.047157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-02-21T01:56:24.709066image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-02-21T01:55:48.663027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:55:52.584573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:55:57.394010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:01.320120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:05.153334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:10.101631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:16.296999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-02-21T01:56:25.197321image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:29.321198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:33.411879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:38.039926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:42.123770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:46.530402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:51.235437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:55:49.204893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:55:53.070964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:55:57.894110image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:01.789249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-02-21T01:56:38.304737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:42.392341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-02-21T01:55:49.481843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:55:53.320918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-02-21T01:56:29.775692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-02-21T01:56:51.717098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:55:49.743837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:55:53.590757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:55:58.413953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:02.301444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:06.192102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:11.151636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:17.278441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:21.728972image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:25.903938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:30.045973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:34.572775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:38.849688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-02-21T01:56:17.541858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:22.115556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:26.146364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:30.298221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:34.942012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:39.110611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:43.162785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:48.019412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:52.256037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-02-21T01:56:26.370435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:30.559190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:35.309903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:39.392392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-02-21T01:56:12.011971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-02-21T01:56:03.318541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-02-21T01:56:12.273356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:18.301115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:23.183209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:26.860987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:31.111661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:35.976802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:39.926531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:43.896308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-21T01:56:48.922715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-02-21T01:57:07.265967image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
AgeBMICLASSGenderHeightLocationWeightbp.1dbp.1scholframeglyhbhdlhipidratiostab.glutime.ppnwaist
Age1.0000.0140.2690.071-0.0670.000-0.0070.0840.4670.2830.1760.430-0.0510.036-0.0680.1910.339-0.0400.166
BMI0.0141.0000.0340.262-0.2480.0000.8350.1950.1600.1290.3140.211-0.2440.878-0.0180.3150.184-0.0270.806
CLASS0.2690.0341.0000.0000.0510.0840.1670.0220.2490.1400.1040.734-0.1800.158-0.0600.2240.5250.0370.269
Gender0.0710.2620.0001.0000.7090.0000.1010.0590.070-0.0280.2050.062-0.135-0.2820.0170.1110.0370.046-0.047
Height-0.067-0.2480.0510.7091.0000.0410.2780.0560.012-0.1180.1180.036-0.140-0.115-0.0060.0650.0280.0080.061
Location0.0000.0000.0840.0000.0411.000-0.0040.085-0.0070.0900.118-0.0770.0180.0810.3930.0090.003-0.185-0.033
Weight-0.0070.8350.1670.1010.278-0.0041.0000.1850.1490.0650.3090.229-0.3240.807-0.0230.3540.206-0.0260.839
bp.1d0.0840.1950.0220.0590.0560.0850.1851.0000.5900.1830.0000.0530.0570.1880.0410.0370.086-0.0540.193
bp.1s0.4670.1600.2490.0700.012-0.0070.1490.5901.0000.2080.1590.285-0.0300.177-0.0050.1210.272-0.0400.227
chol0.2830.1290.140-0.028-0.1180.0900.0650.1830.2081.0000.0600.2290.1460.1160.0220.4000.1350.0010.117
frame0.1760.3140.1040.2050.1180.1180.3090.0000.1590.0601.000-0.2700.236-0.4030.183-0.286-0.209-0.111-0.491
glyhb0.4300.2110.7340.0620.036-0.0770.2290.0530.2850.229-0.2701.000-0.1920.225-0.0790.2920.5310.0310.313
hdl-0.051-0.244-0.180-0.135-0.1400.018-0.3240.057-0.0300.1460.236-0.1921.000-0.2190.041-0.813-0.2020.035-0.307
hip0.0360.8780.158-0.282-0.1150.0810.8070.1880.1770.116-0.4030.225-0.2191.0000.0490.2950.200-0.0570.833
id-0.068-0.018-0.0600.017-0.0060.393-0.0230.041-0.0050.0220.183-0.0790.0410.0491.000-0.033-0.053-0.173-0.027
ratio0.1910.3150.2240.1110.0650.0090.3540.0370.1210.400-0.2860.292-0.8130.295-0.0331.0000.248-0.0190.371
stab.glu0.3390.1840.5250.0370.0280.0030.2060.0860.2720.135-0.2090.531-0.2020.200-0.0530.2481.000-0.1030.246
time.ppn-0.040-0.0270.0370.0460.008-0.185-0.026-0.054-0.0400.001-0.1110.0310.035-0.057-0.173-0.019-0.1031.000-0.007
waist0.1660.8060.269-0.0470.061-0.0330.8390.1930.2270.117-0.4910.313-0.3070.833-0.0270.3710.246-0.0071.000

Missing values

2024-02-21T01:56:53.168880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-21T01:56:53.747301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-02-21T01:56:54.200977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idcholstab.gluhdlratioglyhbLocationAgeGenderHeightWeightBMIframebp.1sbp.1dwaisthiptime.ppnCLASS
01000203.08256.03.64.31Buckingham46female62.0120.021.94589medium118.059.029.038.0720.0N
11001165.09724.06.94.44Buckingham29female64.0218.037.41553large112.068.046.048.0360.0N
21002228.09237.06.24.64Buckingham58female61.0249.547.13746large190.092.049.057.0180.0N
3100378.09312.06.54.63Buckingham67male67.0120.018.79260large110.050.033.038.0480.0N
41005249.09028.08.97.72Buckingham64male68.0181.027.51795medium138.080.044.041.0300.0Y
51008248.09469.03.64.81Buckingham34male71.0188.026.21781large132.086.036.042.0195.0N
61011195.09241.04.84.84Buckingham30male69.0185.527.39057medium161.0112.046.049.0720.0N
71015227.07544.05.23.94Buckingham37male59.0170.034.33209mediumNaNNaN34.039.01020.0N
81016177.08749.03.64.84Buckingham45male69.0166.024.51124large160.080.034.040.0300.0N
91022263.08940.06.65.78Buckingham55female63.0200.035.42454small108.072.045.050.0240.0Y
idcholstab.gluhdlratioglyhbLocationAgeGenderHeightWeightBMIframebp.1sbp.1dwaisthiptime.ppnCLASS
38041075221.012648.04.65.530000Louisa59female62.0173.531.73010medium130.078.039.045.060.0N
38141078210.08181.02.64.960000Louisa78male66.0145.023.40106large110.070.038.039.0540.0N
38241253192.08569.02.84.380000Louisa51male65.0143.023.79385large130.0110.0NaNNaN60.0N
38341254169.010458.02.94.820000Louisa25female60.0150.029.29167medium140.095.040.042.060.0N
38441500179.08550.03.64.990000Louisa37male66.0137.522.19066medium190.094.033.039.0480.0N
38541503301.090118.02.64.280000Louisa89female61.0118.022.29347medium218.090.031.041.0210.0N
38641506296.036946.06.416.110001Louisa53male69.0181.026.72611medium138.094.035.039.0210.0Y
38741507284.08954.05.34.390000Louisa51female63.0154.027.27690medium140.0100.032.043.0180.0N
38841510194.026938.05.113.630000Louisa29female69.0168.524.88038small120.070.033.040.020.0Y
38941752199.07652.03.84.490000Louisa41female63.0193.034.18468medium120.078.041.048.0255.0N